Being in the same tweet as Olke Uhlenbeck is a true honor. Thank you @jhdcate.bsky.social

Excited to announce a new collaborative preprint about a structural concept 'local stability compensation'. This states structurally important motifs must be flanked by more stable helices. We observe this effect natural occurring RNAs and experimentally evaluate it. www.biorxiv.org/content/10.1...

RNA-Puzzles Round V: blind predictions of 23 RNA structures - Nature Methods The results of the Fifth RNA-Puzzles contest highlights advances in RNA three-dimensional structure prediction and uncovers new insights into RNA folding and structure.

It's great to see the latest RNA puzzles paper has come out.
www.nature.com/articles/s41...

Its a must-read for those interested in RNA structure prediction!

Hi @incarnatolab.bsky.social thanks very much. Yes we ran this already and are processing now. There are many interesting things to checkout.

@rodrigo-reis.bsky.social We are doing that right now. I totally agree.

@gallardo-seq.bsky.social yes, I absolutely agree.

Our results provide a quantitative framework for interpreting DMS reactivity patterns in RNA. This enables more sophisticated structure prediction algorithms that consider local sequence context, non-canonical interactions, and three-dimensional features - moving beyond simple base-pair predictions.

Most significantly, we discover that DMS reactivity correlates strongly with atomic distances in non-canonical base pairs. These quantitative relationships demonstrate that DMS chemical mapping data encodes detailed information about RNA 3D structure.

Our results provide a quantitative framework for interpreting DMS reactivity patterns in RNA. This enables more sophisticated structure prediction algorithms that consider local sequence context, non-canonical interactions, and three-dimensional features - moving beyond simple base-pair predictions.

Our results provide a quantitative framework for interpreting DMS reactivity patterns in RNA. This enables more sophisticated structure prediction algorithms that consider local sequence context, non-canonical interactions, and three-dimensional features - moving beyond simple base-pair predictions.

Most significantly, we discover that DMS reactivity correlates strongly with atomic distances in non-canonical base pairs. These quantitative relationships demonstrate that DMS chemical mapping data encodes detailed information about RNA 3D structure.

We find that 11% of non-Watson-Crick nucleotides show protection from DMS similar to Watson-Crick pairs. This protection stems from hydrogen bonding and reduced solvent accessibility. Sequence context can alter reactivity up to 100-fold in specific non-canonical pairs.

We analyzed flanking WC pairs and found structural features that determine their DMS reactivity. A-U pairs are 19-fold more reactive than G-C pairs, purine neighbors increase reactivity, and junction asymmetry correlates with higher reactivity.

Analysis of our comprehensive dataset reveals DMS reactivity exists on a continuous spectrum rather than discrete states. We observe ~10% overlap between Watson-Crick and non-Watson-Crick nucleotides, demonstrating that simple reactivity thresholds cannot reliably determine base-pairing status.

To correlate DMS reactivity with RNA structure, we built a massive library of 7,500 RNA constructs containing multiple junctions with known 3D structures. Our measurements are highly reproducible (R²=0.99), span four orders of magnitude, and reveal that RNA motifs have unique DMS profiles.

A quantitative framework for structural interpretation of DMS reactivity Dimethyl sulfate (DMS) chemical mapping is widely used for probing RNA structure, with low reactivity interpreted as Watson-Crick (WC) base pairs and high reactivity as unpaired nucleotides. Despite i...

🧬 Excited to share our new preprint! DMS chemical mapping, a key technique for studying RNA structure. Everyone assumes low DMS reactivity = Watson-Crick , high = non-WC. However, analyzing 7,500 RNA structures containing known 3D structures reveals it's not that simple. doi.org/10.1101/2024...

@incarnatolab.bsky.social Same. Thanks for the shoutout. It's nice to see you here.

I hope this place is more positive and science-friendly than Twitter. I look forward to chatting about awesome science.